Economic

Research

Service

Economic

Research 

Report 

Number 179

December 2014

United States Department of Agriculture

Menu Labeling Imparts New 

Information About the Calorie 

Content of Restaurant Foods

Hayden Stewart, Jeffrey Hyman, and Diansheng Dong
Economic Research Service 

www.ers.usda.gov

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Stewart, Hayden. Jeffrey Hyman and Diansheng Dong. Menu Labeling Imparts New 

Information About the Calorie Content of Restaurant Foods, ERR-179, U.S. Department 

of Agriculture, Economic Research Service, December 2014.

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United States Department of Agriculture

Economic 

Research 

Service

Economic

Research 

Report 

Number 179

December 2014

Abstract

Restaurant foods are typically higher in calories than meals consumed at home. Menu labeling 

regulations by the U.S. Food and Drug and Administration aim to inform consumers about 

the calorie content of menu items. However, some consumers may already be making at least 

partially informed decisions. For example, as a rule of thumb, a consumer may be aware that 

deep-fried foods are higher in calories. He or she may also know to avoid side dishes like 

French fries and onion rings. Indeed, it has been argued that some consumers can already identify which foods best satisfy their needs and wants and gain little new information from menu 

labeling. In this study, following research in marketing science and behavioral economics, we 

assume that a representative consumer employs rules-of-thumb nutrition knowledge to judge the 

calorie content of restaurant foods when explicit information is unavailable. We then investigate whether rules of thumb accurately predict the calorie content of 361 meals sold by 2 major 

fast-food restaurants and 5,752 meals sold by 5 major full-service restaurants. Results show 

that some simple rules of thumb are fairly reliable predictors of actual calorie content. They 

and other information available at the point of sale also explain about half of the total variation 

in calories in restaurant foods. Nonetheless, we find that menu labeling still imparts substantial new information. In particular, it is likely that many Americans are already able to make 

crude choices between high- and low-calorie foods, based on their pre-existing understandings 

of nutrition. Menu labeling allows them to make finer adjustments in their food choices and 

behavior, if they wish to.

Keywords: Menu labeling, restaurant menu, calorie, food choices, obesity, nutrition information, menu board, Patient Protection and Affordable Care Act

Acknowledgments

The authors thank Dean Jolliffe of the World Bank, Biing Hwan Lin of the U.S. Department 

of Agriculture/Economic Research Service, Professor Joshua Berning, University of Georgia, 

and Professor Brenna Ellison, University of Illinois for review comments including assistance with the econometric model. We also thank Andrew Stivers of the U.S. Food and Drug 

Administration for background information and review comments. Finally, we thank Priscilla 

Smith for editing and Curtia Taylor for design and layout, both in USDA/ERS.

Hayden Stewart, Jeffrey Hyman, and Diansheng Dong

Menu Labeling Imparts New 

Information About the Calorie 

Content of Restaurant Foods
Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

The Debate Over Menu Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Does Menu Labeling Affect Food Choices? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Theoretical Framework Behind the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Data Collected for the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Rules of Thumb. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Other Sources of Nutrition Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Results and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Marginal Effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Goodness of Fit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

What Consumers May and May Not Understand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

The Consumer’s Level of Nutrition Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Appendix tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
United States Department of Agriculture

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providing timely information on economic and policy 

issues related to agriculture, 

food, the environment,and 

rural America. www.ers.usda.gov

Find the full report 

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gov/publications/erreconomic-researchreport/err179

Hayden Stewart, Jeffrey Hyman, and Diansheng Dong

Menu Labeling Imparts New 

Information About the Calorie 

Content of Restaurant Foods

What Is the Issue? 

U.S. Food and Drug Administration regulations require chain restaurants to clearly and 

prominently display the calorie content of menu items. Major fast-food restaurants, for 

example, need to post the number of calories in burgers, sandwiches, and other foods on 

their menu boards; in full-service restaurants, calories are noted in the menus. Some localities around the United States previously had implemented their own regulations. In New 

York City, where menu labeling has been required since 2008, researchers found that about 

28 percent of the customers at a fast-food eatery adjusted their order after noticing calorie 

information. In Philadelphia, where local menu labeling regulations also had been implemented, 34 percent of the customers at a full-service restaurant adjusted their order after 

noticing calorie information. Whether a particular consumer responds to menu labeling may 

furthermore vary with his or her pre-existing knowledge of nutrition. Behavioral economics 

postulates that, when restaurants do not provide explicit calorie information, consumers may 

use rules-of-thumb nutrition knowledge to judge the calorie content of meals. For example, 

a consumer may know that deep-fried foods are typically higher in calories, whereas meals 

containing fruits and vegetables are generally lower in calories. If such simple rules are 

effective for discriminating among restaurant meals, then some people already may be 

able to identify the foods that best satisfy their needs and wants without the help of menu 

labeling. In this study, we investigate the effectiveness of rules-of-thumb nutrition knowledge 

for judging the calorie content of restaurant meals, and the extent to which menu labeling 

imparts additional information. 

What Did the Study Find?

Prior to the passage of menu labeling laws, when restaurants were not required to provide 

explicit calorie data, Americans may have relied on their own knowledge of nutrition to guess 

the calorie content of foods. This may have worked to some extent since consumers can 

deduce the healthfulness of restaurant foods from some readily observable cues, as noted by 

the American Heart Association (AHA) and the National Heart, Lung and Blood Institute 

December 2014
www.ers.usda.gov

(NHLBI), among others. For example, as a rule of thumb, Americans should expect deep-fried foods to be 

higher in calories. If consumers want to lessen the amount of calories they consume, they also should seek 

out meals rich in fruits and vegetables, and avoid side dishes like French fries and onion rings. 

This study confirms that consumers can discriminate fairly well between low- and high-calorie menu items 

using only rules of thumb. Results show that some simple rules of thumb are fairly reliable predictors of 

actual calorie content. They and other information available at the point of sale also explain about half 

of the total variation in calories in restaurant foods. Nonetheless, rules of thumb are blunt tools, and less 

effective for discriminating among foods that differ modestly in calorie content. For example, a consumer 

who knows some rules of thumb highlighted by the AHA and the NHLBI already should understand that 

a chicken sandwich meal at one fast-food restaurant contains fewer calories when ordered with a side dish 

of applesauce (800 calories) instead of French fries (1,190 calories). However, the same consumer may 

be unaware whether a sandwich featuring a deep-fried chicken fillet is more or less caloric than the same 

restaurant’s signature hamburger, holding constant the side dish (720 calories with the applesauce, 1,110 

with the fries). Similarly, when examining the menu at one full-service restaurant chain, this consumer 

may be unable to discriminate between a chicken breast served with bacon, cheese, and fried potato 

wedges (1,172 calories) and a cheeseburger served with fried potato wedges (1,238 calories).

Using rules of thumb can also lead to suboptimal choices because they are not always correct. For example, 

when dining at a fast-food chain restaurant, consumers should not consider meals less caloric, on average, 

just because they include fruits or vegetables. Indeed, many of the highest calorie burgers and sandwiches 

available at fast-food restaurants include lettuce and other vegetables. Similarly, when comparing the most 

indulgent, highest calorie meals available at a sit-down restaurant, consumers can disregard any rule of 

thumb about deep-fried foods being the more caloric. Deep-fried foods may actually be lower in calories 

than other highest calorie choices (such as a pasta dish). 

Overall, consumers who have a basic knowledge of nutrition learn less new information from menu 

labeling than other consumers. However, prior to the labeling, even these consumers could make only 

crude choices among high- and low-calorie foods. Providing calorie information on menus should help all 

Americans to better assess the healthfulness of restaurant foods as well as the taste, cost, and convenience 

and make finer adjustments in their food choices and behavior, if they wish.

How Was the Study Conducted? 

A representative consumer was assumed to know some rules-of-thumb nutrition knowledge provided by the 

AHA and the NHLBI. We then collected detailed information on 361 meals sold by 2 fast-food chains and 

5,752 meals sold by 6 sit-down restaurant chains. Finally, we conducted several statistical tests to measure 

our representative consumer’s ability to discriminate among these meals using the rules of thumb. The 

more information he or she can figure out about the calories in the restaurant foods, the less new information menu labeling imparts, all else constant. Ordinary least squares and unconditional quantile regression 

models are estimated to gauge whether our representative consumer can understand variation in calories 

between restaurant meals in general as well as between meals that vary modestly in calorie content.
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Introduction

Due to a menu-labeling provision of the 2010 Patient Protection and Affordable Care Act, consumers 

standing in line at fast-food chain restaurants will no longer have to guess the number of calories 

in their favorite foods. They will see the calorie content of various burgers and sandwiches printed 

on the menu board. The situation will be similar at sit-down chain restaurants. Patrons will see 

the calorie content of a steak meal printed on their handheld menus. Already, in the late 2000s, 

several State and local governments enacted menu labeling laws. The first was New York City in 

2008. The Affordable Care Act and corresponding regulations issued by the U.S. Food and Drug 

Administration (FDA) will now require chain restaurants with 20 or more locations to post calorie 

information at all their U.S. eateries in a clear and conspicuous manner, along with a statement 

about an individual’s daily caloric needs to help the public understand the significance of the calorie 

information.1

Prior to the listing of explicit calorie information on menus, Americans may have relied on their 

knowledge of nutrition to guess the calorie content of foods. According to the American Heart 

Association (AHA) and the National Heart, Lung and Blood Institute (NHLBI) of the National 

Institutes of Health, consumers can glean information about calories from some observable 

characteristics of menu items (AHA, 2013; NIH/NHLBI, 2013). As a rule of thumb, consumers 

should expect deep-fried foods to be higher in calories. They should also expect foods with cheese, 

mayonnaise, gravy, or a creamy or buttery topping to be higher in calories. Both organizations 

further believe that consumers can easily use such information. “You can eat heart-healthy,” 

according to the AHA, “if you know what to look for” (AHA, 2013).

The underlying premise of menu-labeling provisions in the 2010 law is that providing consumers 

with more accurate information will help them to make healthier choices. However, according to 

a growing body of research, only a portion of Americans choose lower calorie foods. Elbel et al. 

(2009) interviewed customers at a fast-food restaurant in New York City after the implementation of local menu-labeling requirements. Among customers who noticed the calorie information, 

1 In November 2014, FDA issued final regulations to carry out the 2010 law’s labeling provisions. The regulations 

state that menus and menu boards should include both calorie information and the following statement: “2,000 calories 

a day is used for general nutrition advice, but calorie needs vary.” For more information on the final regulation, see FDA 

(2014). 

Hayden Stewart, Jeffrey Hyman, and Diansheng Dong

Menu Labeling Imparts New 

Information About the Calorie 

Content of Restaurant Foods
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about 28 percent report that it influenced their behavior.2 In Philadelphia, Auchincloss et al. (2013) 

interviewed customers at a full-service restaurant also subject to local menu-labeling requirements. 

Among customers who noticed the calorie information at that restaurant, 34 percent report that it 

influenced their ordering decisions.3 Whether a particular consumer responds to menu labeling may 

depend in part on his or her pre-existing knowledge of nutrition. Ellison et al. (2013) argue that some 

consumers already understand which foods are lower and higher in calories. For these consumers, 

“calorie labels provide little new information” (p. 8).

In this study, building on research in marketing science and behavioral economics, we investigate 

the extent to which rules-of-thumb nutrition knowledge can be used to judge the calorie content of 

restaurant meals and the extent to which menu labeling imparts additional information. First, we 

assume that a representative consumer gleans information about the calorie content of meals from 

some readily observable product characteristics. The AHA and the NHLBI identify the characteristics of restaurant foods likely associated with calorie content. Independent variables are created 

for each of these attributes. One variable, for example, identifies whether an entrée is deep fried or 

similarly prepared. Another identifies whether it includes cheese, mayonnaise, gravy, or a creamy 

or buttery topping. Finally, using ordinary least squares (OLS) and unconditional quantile regression (UQR) (Firpo et al., 2007 and 2009), we test the association between our independent variables 

and the calorie content of meals at fast-food and sit-down restaurants. The more information our 

representative consumer can glean from some simple rules of thumb about the calories in restaurant 

foods, the less new information he or she will learn from menu labeling, all else constant. We find 

that this consumer, who would otherwise rely on heuristics outlined by the AHA and the NHLBI, 

learns both old information and substantial new information. 

2 Elbel et al. (2013) report that 54 percent of consumers in their study noticed the calorie labels.

3 Auchincloss et al. (2013) report that 76 percent of consumers in their study noticed the calorie labels.
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The Debate Over Menu Labeling 

The Patient Protection and Affordable Care Act of 2010 represents the most significant overhaul 

of U.S. food-labeling laws since the implementation of the Nutrition Labeling and Education Act 

(NLEA) in the early 1990s. Under FDA regulations, authorized by the NLEA, food manufacturers 

must place a Nutrition Facts panel on most packaged products. Included on this panel are the size of 

a serving, the number of servings in a package, and calories per serving among other information. 

Kim et al. (2000) find that consumers who read the Nutrition Facts panel eat more fiber, less cholesterol, less sodium, and fewer calories from fat than their counterparts who don’t read the panel. 

Though the growing problem of obesity in the United States did not abate over the 1990s and 2000s, 

despite implementation of the NLEA, Variyam and Cawley (2006) find that label users gained less 

weight than nonusers.4 However, when eating out, consumers generally did not have such explicit 

nutrition information. The NLEA did not apply to restaurant foods. 

Some restaurants did voluntarily provide explicit calorie data prior to the passage of menu-labeling 

laws. Many of these companies posted it online. However, according to Wootan et al. (2006), the 

data were not generally accessible onsite in restaurants. For example, in 2006, only 72 percent of 

McDonald's outlets in Washington, DC, provided instore nutrition information, and 59 percent 

provided it for a majority of items. Moreover, explicit nutrition information appeared on tray liners, 

in pamphlets, on posters, or on 1-page charts, but not on menu boards. Aside from McDonald’s, 

Saelens et al. (2007) inspected 102 fast-food and 115 sit-down restaurants in Atlanta, GA. They 

found that only 6.9 percent of fast-food outlets placed nutrition information on menu boards and 5.2 

percent of sit-down restaurants printed it on menus. Other research shows that, if nutrition information is not prominently displayed at the point of purchase, consumers are unlikely to use it (Roberto 

et al., 2009a). 

Debate over mandatory menu labeling grew over the 1990s and 2000s as it became clear that eating 

out is associated with less healthful food choices (Variyam, 2005). Some believed that providing 

consumers with explicit nutrition information at the point of sale would help them to choose 

healthier food options and reduce unwanted weight gain. In a study supported by the Center for 

Science in the Public Interest (CSPI), for example, Backstrand et al. (1997) asked 256 dieticians 

to estimate the calories in 5 entrées sold by chain restaurants. The dieticians underestimated the 

calories in these foods by between 220 and 680 calories each. Since dieticians guessed inaccurately, Backstrand et al. (1997) conclude that “the average consumer has little chance of accurately 

assessing the healthfulness of meals served in restaurants” (p. 2). The AHA also endorsed menu 

labeling as “an important part of a comprehensive approach to addressing our nation’s obesity 

epidemic...” (AHA, 2009). However, others opposed new regulations partly on the argument that 

most consumers can already identify more and less healthy meals, if they want to. The Center for 

Consumer Freedom (CCF) argued that “America has gotten to the point where we have warning 

labels on just about everything. We don’t need government to tell us the difference between salad 

and a 12-piece bucket of chicken.” (CCF, 2007).

On average, consumers supported menu labeling. National polls conducted before the passage of the 

2010 Act found that support ranged between 67 percent and 83 percent (Roberto et al., 2009b). Now 

4 The result was statistically significant among only non-Hispanic, white women. However, according to Variyam and 

Cawley’s (2006) calculations, the benefits to this subset of the population alone were sufficiently large to outweigh the 

total cost of the NLEA.
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that the legislation is in effect, other research suggests that consumers’ use of menu labels varies 

across demographic groups. Gregory et al. (2014) find that people who already have healthier diets 

and women, in particular, are more likely to use the information.
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Does Menu Labeling Affect Food Choices?

Many Americans care about identifying and choosing healthy foods, but not to the exclusion of taste, 

cost, or convenience, according to researchers. Glanz et al. (1998) asked 2,967 people to rate the 

level of importance they placed on the different characteristics of foods using a scale of 1 (not at all 

important) to 5 (very important). Respondents placed the most importance on taste (4.7) followed 

by cost (4.1), nutrition (3.9), convenience (3.8), and weight control (3.4), on average. Consistent with 

this result, when Jones (2010) studied how customers make choices at restaurants, many focus-group 

participants reported their intention to order tasty foods. These included one participant who likes 

to indulge at restaurants. From a menu with 11 items, she considered a T-bone steak and chicken 

alfredo pasta, even when told that 6 other items were lower in calories. However, because the steak 

had the least calories of the two dishes she liked, she would choose it over the pasta: “Both of these 

things I never make at home, and it would really feel like an indulgence for me. But even among my 

indulgences, it might impact which indulgence I take.” (p. 460)

In theory, menu labeling helps consumers who are weighing the taste, cost, healthfulness, and other 

attributes of different foods to identify the choice that best satisfies their needs and wants. Burton et 

al. (2006) identified several restaurant foods for which consumers tend to underestimate and overestimate calorie content. They then observed consumers’ choices among these foods with and without 

explicit calorie data. Menu labeling reduced the likelihood of purchasing an entrée if actual calories 

exceeded expectations. Purchase intentions remained constant or increased slightly if actual calories 

were consistent with expectations. “For example, if deli sandwiches are perceived as generally lower 

in calories than burger items, but are actually similar calorically, the evidence suggests that sandwich purchases would decrease more relative to burger purchases after menu labeling that shows 

their similar caloric content.” (Krieger and Saelens, 2013, p. 5).

In order for any new information to influence a consumer’s food choices at restaurants, it must also 

be salient in the consumer’s mind. According to Bordalo et al. (2012), consumers examine many 

characteristics of competing products when they choose among goods. However, if consumers are 

surprised by the price or quality of a product under consideration, they may focus their attention on 

that particular attribute. The consumer’s feelings about this one attribute can then have a disproportionate impact on the final choice among products. The researchers use this model of consumer 

behavior to explain many phenomena such as why many people switch from higher to lower grades 

of gasoline when gasoline prices rise. Bordalo et al.’s (2012) model of consumer behavior can also be 

applied to restaurant menu labeling. A consumer may be weighing the taste, cost, and healthfulness 

of two restaurant meals, and initially believe the foods are similar calorically. Menu labeling then 

reveals the true difference in calories. The magnitude of any surprises can be key. For example, a 

consumer might focus on each meal’s healthfulness after discovering a difference of several hundred 

calories. However, a modest discrepancy of 10 or 20 calories might not stand out in the same 

consumer’s mind and that consumer might instead focus on taste or cost.

In practice, according to a growing body of empirical research conducted in restaurants, consumers 

respond heterogeneously to menu labeling. Several studies, including Elbel et al. (2009), Pulos and 

Leng (2010), and Auchincloss et al. (2013), found that a fraction of people responded by ordering 

lower calorie foods. Others exhibited no response at all. Ellison et al. (2013) argued that this partly 

reflects differences in consumers’ knowledge of nutrition. Some Americans already may understand 

fairly well which foods are low and high in calories. The more a consumer understands about nutri-
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tion, the less he or she will be surprised by menu labels and, in turn, the less likely he or she is to 

make different choices. Such consumer heterogeneity may further explain why researchers disagree 

about the size of the average change in calories ordered when consumers are exposed to calorie 

information. Roberto et al. (2010) found that participants in an experimental study consumed 177 

fewer calories, on average, as a result of calorie information. Auchincloss et al. (2013) similarly 

found that consumers at a full-service restaurant ordered 155 fewer calories. By contrast, in Pulos 

and Leng (2010), consumers at 6 different full-service restaurants chose entrées with only 15 fewer 

calories. Bollinger et al. (2011) report that Starbucks’ customers ordered 14 fewer calories per transaction. Elbel et al. (2009) found no evidence of a decrease in average calories ordered among the 

customers at a major fast-food chain. Different people were involved in the various studies.
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Theoretical Framework Behind the Study

When the quality of a food product is not explicitly revealed to consumers, marketing scientists 

believe that consumers may draw their own conclusions based on a small number of observable 

product characteristics. In particular, according to Olson and Jacoby (1972), consumers may focus 

on between four and seven product characteristics that they believe to predict true quality, and which 

they can confidently discern. For example, when evaluating meat at a supermarket, consumers may 

lack the confidence to use information about an animal’s breed, even if it would help to predict 

quality. Instead, Acebron and Dopico (2000) find that consumers focus on color, freshness, visible 

fat, price, and presentation. 

Behavioral economists similarly recognize that consumers use simple mental shortcuts to form 

judgments and make decisions since they do not understand the intricacies of nutrition science 

(Guthrie et al., 1999; Cash and Schroeter, 2010). Simple heuristics, such as rules of thumb, can be 

easily understood and applied in nearly every situation. (“Rule of thumb” is a method of estimating 

a value, based on common sense and experience, that is not intended to be scientifically accurate.) 

However, rules of thumb are not always correct and can lead to suboptimal choices. Thus, it is interesting to ask whether rules of thumb based on the observable attributes of meals are effective for 

judging calorie content at restaurants. 

In this study, we mimic a representative consumer’s situation in the absence of menu labeling. We 

assume that he or she makes judgments about the calorie content of restaurant meals based on 

observable product characteristics. These include a handful of characteristics that, according to 

the AHA and the NHLBI, do indicate a restaurant meal’s calorie content. We begin by examining 

the calorie content of meals at some major chain restaurants. We then test the association between 

calorie content and the identified product characteristics. The more information our representative 

consumer can glean from these characteristics about the calories in restaurant foods, the less new 

information he or she will learn from menu labeling, all else constant. Table 1 provides definitions 

and mean values for the variables used in the study.
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Table 1

Definitions and means of variables used in the study 

Fast-food 

restaurants 

Full-service 

restaurants

CALORIES Number of calories in the meal 726.25 1,087.37

HEALTHCLAIM 1 if a health claim appears on the menu next to the 

entrée; 0 otherwise 0 0.16

SMALLPORTION 1 if entrée represents a reduced-size portion; 0 

otherwise 0.29 0.07

LARGEPORTION 1 if entrée represents an extra-size portion; 0 

otherwise 0.1 0

DEEPFRIED 1 if deep-fried, batter-fried, country-fried, or similarly 

prepared; 0 otherwise 0.43 0.09

HEAVYSAUCE

1 if meal includes cheese, mayonnaise, gravy, or a 

creamy or buttery topping, unless menu specifies 

reduced-calorie versions; 0 otherwise 

0.76 0.6

FRIES 1 if meal comes with french fries, onion rings, or 

similar extras; 0 otherwise 0.41 0.31

FATTYMEAT

1 if meal contains beef, bacon, pork chops, 

pepperoni, sausage, or similar meat products, 

unless menu specifies a lean cut; 0 otherwise

0.55 0.6

NOVEGGIES1 1 if meal includes neither fruits nor vegetables; 0 

otherwise 0.12 0.08

1 For the purposes of this study, we do not treat french fries or onion rings as vegetables. Thus, NOVEGGIES would equal 

one for a meal served with french fries and no other vegetables or fruit. 

Note: Publicly available data were collected in spring 2012. We initially recorded the calories in standard menu items using 

the restaurants’ websites. Stores in Montgomery County, MD were later visited to confirm that online information was 

accurate. We also created the variables SMALLPORTION, LARGEPORTION, and HEALTHCLAIM by observing restaurant 

menus. The American Heart Association (AHA) and the National Heart, Lung and Blood Institute (NHLBI) identify the 

characteristics of restaurant meals indicative of calorie content (AHA, 2013; NIH/NHLBI, 2013). Both organizations focus 

on the same key characteristics, all readily observable to customers. Based on this information, we created DEEPFRIED, 

HEAVYSAUCE, FRIES, FATTYMEAT, and NOVEGGIES. According to the information provided by the AHA and the 

NHLBI, as rules of thumb, we expect that values of 1 are associated with having more calories and values of 0 are 

associated with having fewer calories. 

Source: USDA, Economic Research Service calculations based on publicly available data.
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Data Collected for the Study

In spring 2012, using restaurant companies’ websites, we recorded information about the calorie 

content of standard menu items at a sample of fast-food and sit-down restaurants. The restaurants 

were then visited to confirm that online information was accurate. All data collected for the study 

are publicly available. Each sampled chain operates at least one restaurant in Montgomery County, 

MD, which implemented local labeling requirements in 2010.5 The number of calories in meals at 

these restaurants is denoted CALORIES. 

Meals from two fast-food restaurants were included in the study. We calculated the calories in 361 

meals at these restaurants. Most meals included one entrée and one side dish.6 We did not include 

calories from beverages, desserts, or other extra foods. The distribution of CALORIES for these two 

restaurants is shown in figure 1. Meals in the lower one-third of the distribution have 553 or fewer 

calories. Those in the middle tertile have 554 to 817 calories. Meals in the upper tertile contain 818 

or more calories. The maximum value of CALORIES for our two fast-food restaurants is 1,710. 

A separate sample of meals was collected from six casual dining, sit-down establishments. All of 

these chains offer dinner entrées and serve alcohol. The calories in 5,752 meals at these chains were 

recorded, including calories in entrées and in standard side dishes, but excluding those from beverages, desserts, and other extra foods.7 Figure 2 presents the distribution of CALORIES for our six 

sit-down restaurants. Meals in the first tertile of the distribution have 900 or fewer calories. Meals 

in the middle tertile have 901 to 1,239 calories. In fact, the median meal (1,064 calories) without 

beverages or other extra foods contains about half of the daily energy requirements of a person on a 

2,000-calorie reference diet, or about one-third of the energy requirements of an active, young adult 

male who needs 3,000 calories per day. Items in the upper tertile contain 1,240 or more calories. The 

maximum value of CALORIES for these six restaurants is 2,350. 

Data collected for the study notably include a proportionally smaller number of fast-food meals than 

meals at full-service restaurants. We sampled only two fast-food restaurants for the study because 

each of these restaurants accounts for a proportionally greater share of their segment of the foodservice market than do any two of our full-service restaurants. Also, fast-food restaurants tend to offer a 

narrower range of menu choices than do full-service restaurants. 

5 We were able to obtain complete calorie information for all standard menu items since compliance with local labeling 

laws has been mandatory since January 1, 2011.

6 Meals were generally defined by combining a selected entrée with one side dish. We paired one restaurant’s cheeseburger, for example, with a medium fries. We also paired it with apple slices, a side salad, and a fruit and yogurt parfait. 

Each of these four pairings was defined as a separate meal. Selected entrées at the two fast-food restaurants included 

sandwiches, burgers, wraps, chicken pieces, and salads. Breakfast items were excluded. Only entrée salads were not 

paired with a side dish and assumed to represent a meal by themselves.

7 Meals can include side dishes. For example, a steak entrée might come with a choice of two side dishes at no additional charge. Moreover, the restaurant could allow its customers to select among four possible side dishes, say, French 

fries, steamed vegetables, a baked potato, and rice. In this case, consumers could choose six different combinations of 

two side dishes. In this study, we treat each of these six possibilities as a separate meal, each of the six meals being composed of the steak and one of the possible combinations of side dishes. We do not consider side dishes for which customers have to pay extra money.
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Note: Data include the calories in 361 meals available at 2 fast-food restaurants in spring 2012. We initially recorded the 

calories in menu items using the restaurants’ websites. Restaurants were later visited to confirm that online information 

was accurate. Meals were generally defined by combining a selected entrée with one side dish. We paired one restaurant’s cheeseburger, for example, with a medium portion of fries. We then paired it with apple slices, a side salad, and a 

fruit and yogurt parfait. Each of these four pairings was defined as a separate meal. We do not account for any additional 

calories from beverages, desserts, or other extra foods. 

Source: USDA, Economic Research Service calculations based on publicly available data. 

Figure 1

Distribution of calories in meals at fast-food restaurants

Share of meals

0.30

0.25

0.20

0.15

0.10

0.05

0

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400

Calories in meals

Note: Data include the calories in 5,752 meals available at 6 casual-dining, sit-down restaurants in spring 2012. We 

initially recorded the calories in menu items using the restaurants’ websites. Restaurants were later visited to confirm that 

online information was accurate. Total calories account for those in entrées and in standard side dishes, but do not 

account for beverages, desserts, and other extra foods. 

Source: USDA, Economic Research Service calculations based on publicly available data. 

Figure 2

Distribution of calories in meals at sit-down restaurants

Share of meals

0.30

0.25

0.20

0.15

0.10

0.05

0

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400

Calories in meals
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Rules of Thumb

The AHA and the NHLBI identify the attributes of restaurant foods likely associated with calorie 

content. Both organizations focus on the same key characteristics, all readily observable to a 

restaurant’s customers. Based on this information, we created five binary explanatory variables. 

Each equals 1 for foods that exhibit a particular product characteristic, and 0 (zero) otherwise. 

NOVEGGIES indicates that a meal includes neither vegetables nor fruits. Notably, we do not treat 

French fries or onion rings as vegetables for the purposes of this study. Thus, NOVEGGIES = 1 for 

a meal served with French fries and no other vegetables or fruit. FRIES = 1 if a meal comes with 

French fries or onion rings. DEEPFRIED indicates that an entrée has been deep fried, golden fried, 

or similarly prepared. HEAVYSAUCE indicates that menu items come with cheese or other condiments and toppings that are creamy or buttery, unless explicitly stated in the menu that reducedcalorie versions are used. FATTYMEAT = 1 if a meal contains beef, bacon, pork chops, pepperoni, 

sausage, or similar meat products, unless the menu specifies a lean cut. According to the information 

provided by the AHA and the NHLBI, as rules of thumb, we expect that values of 1 are associated 

with having more calories and values of 0 are associated with having fewer calories. 

Finally, we create an additional variable, TOTALRULES, equal to the number of product attributes suggesting that a meal may be higher in calories. That is, TOTALRULES = FRIES + 

HEAVYSAUCE + FATTYMEAT + NOVEGGIES + DEEPFRIED. In our data, the maximum value 

of TOTALRULES = 4 for both our sit-down and fast-food eateries.

While we do not know the proportion of consumers who understand the information in table 1, other 

research suggests that Americans have some basic knowledge of nutrition no more complicated 

than our five rules of thumb. As described in Guthrie et al. (1999), the 1994 Diet Health Knowledge 

Survey (DHKS) asked consumers about their knowledge of and attitudes toward nutrition.8 As a part 

of this survey, consumers were asked to identify which, in a series of paired foods, was higher in fat 

(hot dogs or ham, yogurt or sour cream, etc.) or higher in saturated fat (liver or T-bone steak, butter 

or margarine, etc.). The researchers found that almost 80 percent of consumers could correctly identify the higher fat food in five of six paired comparisons. About 60 percent were able to identify the 

higher saturated-fat food in three of four food pairs. 

As a preliminary exercise, we test the association between our rules of thumb and the actual 

calorie content of selected restaurant meals. Shown in table 2 are the values of CALORIES and 

TOTALRULES for a small sample of meals. The data suggest that consumers, who know some rules 

of thumb highlighted by the AHA and NHLBI, can figure out that one fast-food chain’s chicken 

sandwich meal contains fewer calories when ordered with applesauce (800 calories) instead of 

French fries (1,190 calories). However, such consumers may not know whether this sandwich is more 

or less caloric than the same restaurant’s signature hamburger, holding constant the side dish (720 

calories with the applesauce and 1,110 calories with the fries). The sandwich includes a deep-fried 

chicken fillet with lettuce, tomatoes, and mayonnaise, while the hamburger includes a grilled beef 

patty with tomatoes, lettuce, pickles, onions, and mayonnaise. TOTALRULES is the same for both 

entrées. Similarly, when dining at one full-service restaurant chain, this consumer may be unable 

to discriminate between a grilled chicken breast served with sautéed mushrooms, bacon, cheese, 

and fried potato wedges (1,172 calories) and a cheeseburger served with lettuce, tomatoes, onions, 

8 USDA’s 1994-1996 Continuing Survey of Food Intakes by Individuals (CSFII) asked a nationally representative survey of individuals to report their food intake on 2 nonconsecutive days. The 1994 DHKS was a telephone followup to this 

survey. It included a sample of CSFII participants. 
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pickles, and fried potato wedges (1,238 calories). Not only is TOTALRULES the same for each 

meal, but the values of DEEPFRIED, HEAVYSAUCE, FRIES, FATTYMEAT, and NOVEGGIES 

are also identical. Consumers need a more nuanced knowledge of nutrition than our rules of thumb 

to discriminate between these meals. 

To further gauge the association between TOTALRULES and CALORIES for all meals, not just for 

the 12 meals examined in table 2, violin plots are created (figs. 3 and 4). As described in Hintze and 

Nelson (1998), violin plots are similar to box plots. They display the center and spread of the data. 

In addition, they reveal the distribution of the data, with its valleys, peaks, and bumps. For example, 

shown in the left-most plot in figure 4 is the distribution of CALORIES for 591 full-service restaurant meals for which TOTALRULES = 0. We expect these meals to represent the lowest calorie 

choices at these restaurants. In fact, when consumers select among these 591 meals, they receive 

a dish with 900 or fewer calories with a probability of 87 percent (513 out of 591). By contrast, as 

shown in the fourth plot from the left in figure 4, among meals for which TOTALRULES = 3, we 

find that 76 percent (738 out of 969) contain 1,200 or more calories. Nonetheless, figure 3 and figure 

4 also suggest that heuristics are a blunt tool. CALORIES still varies widely among meals sharing 

the same values for TOTALRULES.

Table 2

Calorie content of selected meals at two restaurants

CALORIES1 TOTALRULES2

Meals at a selected fast-food restaurant

Deep-fried pieces of chicken (6 pieces) with applesauce 340 1

Salad with grilled chicken, croutons, and fat-free dressing 410 1

Hamburger sandwich (1/4 lb grilled beef patty) with applesauce 720 2

Chicken sandwich (deep-fried fillet) with applesauce 800 2

Hamburger sandwich (1/4 lb grilled beef patty) with medium french fries 1,110 3

Chicken sandwich (deep-fried fillet) with medium french fries 1,190 3

Meals at a selected sit-down restaurant

Steak (8 oz prime rib) with chicken tortilla soup and mixed vegetables 726 1

Steak (9 oz sirloin) with grilled shrimp, asparagus, and mixed vegetables 777 1

Steak (8 oz prime rib) with chicken tortilla soup and sweet potato fries 1,075 2

Steak (9 oz sirloin) with grilled shrimp, asparagus, and sweet potato fries 1,126 2

Grilled chicken breast served with bacon, cheese, and fried potato wedges 1,172 3

Cheeseburger served with fried potato wedges 1,238 3

1CALORIES does not account for any additional calories from beverages, desserts, or other extra foods. 

2Using information supplied by the American Heart Association and the National Heart, Lung, and Blood Institute, we 

identified the characteristics of restaurant meals that may be associated with the calorie content. These characteristics are 

detailed in table 1. TOTALRULES equals the total number of product characteristics according to which a meal may be high 

in calories.

Lb = pound

Oz = ounce

Note: Publicly available data were collected in spring 2012. We initially recorded the calories in standard menu items using 

the restaurants’ websites. Stores were later visited to confirm that online information was accurate. 

Source: USDA, Economic Research Service calculations based on publicly available data.
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Other Sources of Nutrition Information

While most restaurants traditionally have not posted explicit nutrition data at the point of sale, they 

have provided limited information about the healthfulness of their menu items in other ways. Placing 

nutrition claims next to selected entrées has been one of these practices. Claims have included statements, such as “low calorie” or “low fat,” or consisted of logos like the AHA’s “Heart Check” mark. 

The 6 sit-down restaurants in this study all placed statements next to selected foods claiming that 

a particular food contained or could be prepared to contain less than a certain number of calories, 

such as 550 or 600.9 We created the binary variable, HEALTHCLAIM, to account for these cases. 

This variable = 1 for menu items associated with a health claim and 0 otherwise. 

9 This is aside from numeric calorie labels, which were not available on otherwise similar menus provided by the 

restaurants under study in communities without local menu-labeling laws in 2012.

Note: To discriminate between lower and higher calorie meals, when explicit calorie information is not provided, a 

consumer may use rules of thumb (a method of estimating a value, based on common sense and experience). In this 

study, we create five binary variables to account for some key characteristics of restaurant meals. These include 

DEEPFRIED, HEAVYSAUCE, FRIES, FATTYMEAT, and NOVEGGIES. Full definitions are provided in table 1. As rules of 

thumb, we expect that values of 1 are associated with having more calories and values of 0 are associated with having 

fewer calories. Finally, we created an additional variable, TOTALRULES, which equals the sum of DEEPFRIED, HEAVYSAUCE, FRIES, FATTYMEAT, and NOVEGGIES. In other words, TOTALRULES equals the number of product attributes 

according to which a meal may be high in calories. The first violin plot above includes meals for which TOTALRULES = 0. 

The second column includes meals for which TOTALRULES = 1. And, finally, the fifth column includes meals for which 

TOTALRULES = 4, the maximum value of the variable.

Source: USDA, Economic Research Service calculations based on publicly available data.

Figure 3

Rules of thumb and calorie content of fast-food meals, violin plot 

Number of calories

0

500

1,000

1,500

2,000

0 1 2 3 4

Number of rules suggesting food is high in calories
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Finding the right size meal at a restaurant is also important to calorie control. Not only do Young 

and Nestle (2003, 2007) find that restaurant meals commonly exceed standard serving sizes,10 but 

restaurants have been offering larger portion sizes over time. One notable example is Burger King’s 

introduction of a Triple Whopper sandwich in 2005. This entrée contains 1,140 calories and weighs 

16 ounces (oz) including the meat, bun, vegetables, and other ingredients. By contrast, the traditional, single-patty Whopper sandwich contains 670 calories and weighs 10 oz. According to Young 

and Nestle (2003), fast-food restaurants now market some foods in portion sizes two to five times 

larger than their original size. However, many restaurants still help consumers to identify and order 

smaller portion sizes, if they want. Both of the fast-food restaurants in this study offer smaller sized 

sandwiches and burgers weighing 5.3 oz or less. Customers can also buy 4 or 6 pieces of deep-fried 

chicken instead of the 10- or 20-piece sizes. Similarly, some of the sit-down restaurants under study 

10 The Nutrition Facts panel reports serving sizes. These quantities are based on the reference amount customarily 

consumed per eating occasion (RACC) as reported in the 1970s and 1980s by Americans participating in food consumption surveys conducted by the U.S. Department of Agriculture. 

Note: To discriminate between lower and higher calorie meals, when explicit calorie information is not provided, a 

consumer may use rules of thumb (a method of estimating a value, based on common sense and experience). In this 

study, we create five binary variables to account for some key characteristics of restaurant meals. These include 

DEEPFRIED, HEAVYSAUCE, FRIES, FATTYMEAT, and NOVEGGIES. Full definitions are provided in table 1. As rules of 

thumb, we expect that values of 1 are associated with having more calories and values of 0 are associated with having 

fewer calories. Finally, we created an additional variable, TOTALRULES, which equals the sum of DEEPFRIED, HEAVYSAUCE, FRIES, FATTYMEAT, and NOVEGGIES. In other words, TOTALRULES equals the number of product attributes 

according to which a meal may be high in calories. The first violin plot above includes meals for which TOTALRULES = 0. 

The second column includes meals for which TOTALRULES = 1. And, finally, the fifth column includes meals for which 

TOTALRULES = 4, the maximum value of the variable.

Source: USDA, Economic Research Service calculations based on publicly available data.

Figure 4

Rules of thumb and calorie content of sit-down restaurant meals, violin plot 

Number of calories

500

1,000

1,500

2,000

2,500

0 1 2 3 4

Number of rules suggesting food is high in calories
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sell a 6-oz “petite” steak instead of a 10- or 14-oz steak. Some also offer half racks of ribs instead 

of full racks. The variable SMALLPORTION = 1 for meals featuring a small-sized entrée and 0 for 

other meals. LARGEPORTION = 1 for meals featuring extra-sized entrées.
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Regression Models

To mimic our representative consumer’s situation in the absence of menu-labeling laws, when 

calorie information is not explicitly reported, we regress CALORIES on our explanatory variables. A real-life consumer would need to know the values of our model’s parameters to predict the 

number of calories in the meals. As discussed below, we do not necessarily expect consumers to 

know these values. However, if the rules of thumb highlighted by the AHA and NHLBI are effective for discriminating between lower and higher calorie choices, then consumers who know this 

information may still be less surprised by explicit calorie data. Thus, we focus on the marginal 

effects of DEEPFRIED, HEAVYSAUCE, FRIES, FATTYMEAT, and NOVEGGIES. Our goal is 

to determine whether the variables have a consistent and statistically significant association with 

CALORIES. If so, these product characteristics could form the basis of rules of thumb that, in turn, 

could reliably identify low- from high-calorie menu choices. We also examine the goodness-of-fit 

statistic, R2. 

An ordinary least squares (OLS) regression of CALORIES on our explanatory variables produces 

an estimate of the model, CALORIES = Xβ + ε, where ε is a stochastic residual with mean 0 and 

constant variance, β contains the unknown parameters, and X includes a constant, DEEPFRIED, 

HEAVYSAUCE, FRIES, FATTYMEAT, NOVEGGIES, SMALLPORTION, LARGEPORTION, 

and HEALTHCLAIM. For a model without higher order polynomial terms, as is well known, β

equals the marginal effect of X on the population-average value of CALORIES, all else constant. 

Thus, we can ask questions like “How much higher in calories is a deep-fried food, on average, 

holding constant other attributes of the food?” 

However, since OLS parameters measure the average association between CALORIES and each 

of the five product characteristics under study, OLS results do not measure these associations 

at particular points on the distribution of CALORIES. The marginal effects of DEEPFRIED, 

HEAVYSAUCE, FRIES, FATTYMEAT, and NOVEGGIES are not necessarily the same at all 

points. Also, the collective explanatory power of the variables, as measured by R2, may vary across 

the distribution of CALORIES. Indeed, as shown in table 2, heuristics appear to be blunt tools 

capable of separating low- and high-calorie menu items, but unable to more finely discriminate 

between items that differ modestly in calorie content. To measure the association between our 

explanatory variables and CALORIES at specified points within the middle, lower, and upper tails 

of the distribution of CALORIES, we use Firpo et al.’s (2007, 2009) unconditional quantile regression (UQR) method in addition to OLS. This method has been recently applied in obesity research, 

among other areas. Jolliffe (2011) uses UQR to investigate the relationship between income and 

body mass index (BMI). He finds that income increases the BMIs of underweight Americans and 

decreases those of obese Americans. 

Estimating an UQR model involves transforming one’s dependent variable. The newly transformed 

variable is called the recentered influence function (RIF). In this study, CALORIES must be transformed. As detailed in Firpo et al. (2007, 2009), the formula is: 

RIF(calories; qτ

) = qτ + (τ – I{calories ≤ qτ })/fCALORIES(qτ) 

where qτ

 is the value of CALORIES at the τth quantile of the distribution, fCALORIES (qτ) denotes 

the value of that distribution evaluated at qτ

, and the indicator function I{calories ≤ qτ } equals 1 if 

CALORIES is less than or equal to qτ

 and 0 otherwise. The variable RIF can next be regressed on 
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X to estimate the model, RIF = αX + ν where ν is a stochastic residual with mean zero and constant 

variance, and α contains the parameters to be estimated.11 For a model without higher order polynomial terms, α equals the marginal effect of X on the value of CALORIES at the τth quantile of 

the distribution, all else constant. Thus, we can ask questions like “What is the association between 

calorie content and deep frying at the 10th, 30th, 60th, and 90th percentiles of CALORIES, all else 

constant?”

11 Firpo et al. (2009) label this specification of their model RIF-OLS. We used a Stata ado file provided by Nicole 

Fortin at http://faculty.arts.ubc.ca/nfortin/datahead.html. 
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Results and Findings

OLS and UQR models were estimated for fast-food and sit-down restaurants (tables 3 and 4). Below, 

we examine results for each type of restaurant. We then consider the amount of information that 

consumers may be able to glean about the calorie content of the meals, given their level of nutrition 

knowledge. Finally, we consider other specifications of our model that embody alternative assumptions about consumers and ask whether our key results are significantly changed.

Marginal Effects

Estimation results show that DEEPFRIED, HEAVYSAUCE, FRIES, FATTYMEAT, and 

NOVEGGIES are all associated with CALORIES and may likewise form the basis of rules of 

thumb that, in turn, could be used to somewhat reliably decipher low- from high-calorie menu 

choices. In the first columns of tables 3 and 4, OLS parameter estimates are mostly significant and 

reveal positive associations between CALORIES and each product characteristic. For example, 

ordering a meal with French fries or onion rings increases the calories in a fast-food meal by 327 

and the calories in a sit-down restaurant meal by 269, on average. 

However, rules of thumb do not always work as expected. Consumers at fast-food restaurants should 

not automatically assume that meals with fruits or vegetables are less caloric. The OLS parameter 

estimate on NOVEGGIES is negative (see table 3). To be sure, most of the higher calorie burgers 

and sandwiches offered by our two fast-food chains come with lettuce and other vegetables. 

Our results also confirm that rules of thumb are much less effective for discriminating among meals 

that differ modestly in calorie content. Only FRIES is statistically significant at the 60th and 90th 

percentiles of CALORIES for fast-food meals (see table 3). All other rules of thumb lack power for 

choosing among the highest calorie meals at that type of restaurant. 

Similarly, when choosing among the highest calorie choices at a sit-down restaurant, deep fried 

foods may actually be relatively lower in calories than the other highest calorie choices. The 

marginal effect of DEEPFRIED is negative at the 90th percentile of CALORIES (see table 4). 

Notably, after further investigation, we found that this result was not robust. The coefficient was 

statistically insignificant if we excluded meals sold by any one of three chain restaurants from the 

data set. Nonetheless, when choosing among the highest calorie meals at a sit-down restaurant, 

consumers can still disregard the rule of thumb about deep-fried foods being even more caloric. 

Goodness of Fit

Goodness-of-fit statistics reveal that our five rules of thumb along with SMALLPORTION, 

LARGEPORTION, and HEALTHCLAIM explain half or more of the overall variation in 

CALORIES. As shown in the first columns of tables 3 and 4, R2 is 0.76 in our OLS model for fastfood meals and 0.5 in our OLS model for sit-down restaurant meals. While our model’s predictive power is substantial, it still leaves much of the variation in CALORIES unexplained. Moreover, 

according to our UQR results, R2 tends to be lower near the tails of the distribution of CALORIES 

than around its center. For meals at fast-food restaurants, it is 0.21 at the 10th percentile (see table 3). 

For sit-down restaurants, it is 0.18 at the 10th percentile and 0.14 and the 90th percentile (see table 4). 
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Table 3

Estimation results for calories in fast-food meals 

OLS UQR

(10th centile)

UQR

(30th centile)

UQR

(60th centile)

UQR

(90th centile)

Constant 501.63*** 253.85*** 317.39*** 611.79*** 853.62***

(25.69) (62.41) (55.08) (50.56) (65.13)

DEEPFRIED 84.66** 69.82 147.00*** 61.10 63.49

(21.84) (57.04) (46.16) (47.08) (73.02)

HEAVYSAUCE 59.10** 103.91** 61.84* 34.42 10.92

(19.52) (43.55) (35.99) (34.91) (58.96)

FRIES 327.41*** 154.08*** 311.11*** 340.59*** 437.69***

(16.97) (26.72) (26.58) (35.10) (82.50)

FATTYMEAT 63.58*** 34.63 126.67*** 54.81 26.71

(22.50) (61.00) (47.49) (47.51) (59.21)

NOVEGGIES -66.05** 68.69*** 42.68 -15.92 -308.70***

(26.44) (29.85) (35.92) (53.51) (96.38)

SMALLPORTION -219.61*** -211.99*** -282.59*** -299.98** -149.96***

(17.04) (40.47) (31.66) (31.17) (42.09)

LARGEPORTION 470.18*** 19.34*** 101.75*** 375.12** 1,350.62***

(25.82) (62.41) (33.60) (37.22) (65.62)

R2 0.76 0.21 0.41 0.47 0.47

Hit-and-miss analysis

All combinations of 2 meals: 79.3 percent correctly predicted 

Combinations of meals with 200(+) calorie difference: 91.4 percent correctly predicted 

OLS = Ordinary least squares regression

UQR = Unconditional quantile regression

R2 = Proportion of the variation in the dependent variable that is explained by the independent variables

Hit-and-miss analysis = A measure of the predictive accuracy of the model calculated using OLS results to estimate the 

number of calories in each meal. A selected meal’s predicted calorie content was then compared against that of the other 

meals. This process was repeated until all possible combinations of two different meals had been compared. Our model 

correctly predicted whether a selected meal was higher, lower, or equal in calories to another selected meal for 79.3 

percent of all pairwise comparisons. Moreover, if we restricted our hit-and-miss analysis to only combinations of 2 meals 

with an actual difference of 200 or more calories, then the percentage correctly predicted increased to 91.4 percent. 

Note: Standard errors are in parentheses. Statistically significant at the ***1-percent, **5-percent, and *10-percent levels.

Source: USDA, Economic Research Service calculations based on publicly available data.
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What Consumers May and May Not Understand 

Product characteristics identified by the AHA and NHLBI as being indicative of the calorie content 

of restaurants foods—DEEPFRIED, HEAVYSAUCE, FRIES, FATTYMEAT, and NOVEGGIES—

are significantly associated with CALORIES for the most part. As such, they may underlie rules of 

thumb that, in turn, identify low- from high-calorie menu items to some extent. To gauge our representative consumer’s ability to distinguish between higher and lower calorie menu items, we further 

test the discriminatory power of our model in the spirit of a hit-and-miss analysis. Specifically, we 

used our OLS results to predict the number of calories in each of our 361 fast-food meals. One meal 

was then selected. We compared its predicted calorie content against the predicted calorie content 

Table 4

Estimation results for calories in sit-down restaurant meals 

OLS UQR

(10th centile)

UQR

(30th centile)

UQR

(60th centile)

UQR

(90th centile)

Constant 718.25*** 370.86*** 425.13*** 721.19*** 1,258.58***

(7.33) (15.84) (11.86) (10.07) (11.68)

DEEPFRIED 131.53*** 186.18*** 305.07*** 129.83*** -69.42***

(11.94) (11.03) (16.69) (21.83) (21.70)

HEAVYSAUCE 145.45*** 85.24*** 160.35*** 192.07*** 140.66***

(6.83) (10.84) (10.08) (11.62) (13.63)

FRIES 269.07*** 170.92*** 297.05*** 284.92*** 317.31***

(7.30) (7.78) (9.73) (12.43) (19.81)

FATTYMEAT 339.81*** 256.71*** 395.18*** 433.03*** 232.66***

(6.93) (11.36) (11.11) (11.58) (14.25)

NOVEGGIES 236.85*** 157.87*** 230.02*** 261.18*** 242.75***

(13.02) (12.38) (16.98) (24.46) (39.24)

HEALTHCLAIM -70.10*** -39.29** -4.20 -106.13*** -84.90***

(9.82) (18.27) (17.29) (16.25) (15.91)

SMALLPORTION -341.63*** -183.23*** -364.23*** -464.81*** -283.75***

(12.83) (23.30) (22.36) (17.76) (14.90)

R2 0.50 0.18 0.36 0.34 0.14

Hit-and-miss analysis

All combinations of 2 meals: 71.7 percent correctly predicted 

Combinations of meals with 200(+) calorie difference: 80.4 percent correctly predicted 

OLS = Ordinary least squares regression

UQR = Unconditional quantile regression

R2 = Proportion of the variation in the dependent variable that is explained by the independent variables

Hit-and-miss analysis = A measure of the predictive accuracy of the model calculated using OLS results to estimate the 

number of calories in each meal. A selected meal’s predicted calorie content was then compared against that of the other 

meals. This process was repeated until all possible combinations of two different meals had been compared. Our model 

correctly predicted whether a selected meal was higher, lower, or equal in calories to another selected meal for 71.7 

percent of all pairwise comparisons. Moreover, if we restricted our hit-and-miss analysis to only combinations of 2 meals 

with an actual difference of 200 or more calories, then the percentage correctly predicted increased to 80.4 percent. 

Note: Standard errors are in parentheses. Statistically significant at the ***1-percent, **5-percent, and *10-percent levels.

Source: USDA, Economic Research Service calculations based on publicly available data.
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of each of the other 360 meals. The same process was next repeated for another fast-food meal 

until all possible combinations of two different fast-food meals had been compared. In total, our 

model correctly identified whether a selected meal was higher, lower, or equal in calories to another 

selected meal for 79 percent of all pairwise comparisons. Moreover, among comparisons with an 

actual difference in calories of 200 or more between the 2 meals, the percentage correctly predicted 

is 91 percent. For meals at sit-down restaurants, these shares were 72 percent and 80 percent, 

respectively.

It appears that consumers with some basic knowledge of nutrition can discriminate fairly well 

between low- and high-calorie restaurant meals, especially if the foods contain substantially 

different numbers of calories. However, inherent in our regression models are some assumptions 

about our representative consumer. Implicitly, when multiple rules are “broken,” it is assumed that a 

consumer compounds calorie estimates. For example, when judging an item at a sit-down restaurant 

that is both deep fried and served with French fries, our representative consumer thinks to add 131 

calories for the entrée being deep fried and another 269 calories for the side dish of French fries 

(400 extra calories in total). This clearly requires our consumer to have a pretty good idea of the 

relationship between each rule of thumb and the calorie content of the meals (i.e., he or she must 

know our model coefficients). However, real-life consumers who know the information outlined 

by the AHA and the NHLBI still may underestimate the size of the coefficients in our model. For 

example, they may believe that deep frying adds only 50 calories and ordering a side of French 

fries increases total calories by only 100, all else constant. If so, as argued by organizations like the 

Center for Science in the Public Interest, consumers would tend to underestimate the total amount of 

calories in restaurant meals. Nonetheless, as argued by organizations like the Center for Consumer 

Freedom, they could still guess fairly well whether a menu item is higher or lower in calories than 

other menu items, especially when the foods have substantially different numbers of calories. 

The Consumer’s Level of Nutrition Knowledge 

To gain additional insights on our representative consumer’s ability to judge the calorie content of 

restaurant meals, if he or she has a less or more nuanced knowledge of nutrition, we examine four 

alternative specifications of the model. In the first of these alternative specifications, our representative consumer understands that DEEPFRIED, HEAVYSAUCE, FRIES, FATTYMEAT, and 

NOVEGGIES are all associated with an item being more caloric. However, he or she does not 

know that ordering a side dish of French fries increases the total number of calories in a meal more 

than does adding a slice of cheese, all else constant. FRIES and the four other rules are assumed 

to be equally important. Thus, our consumer simply counts the total number of product characteristics according to which a meal may be higher in calories. A model embodying this assumption 

is estimated by regressing CALORIES on a constant term, TOTALRULES, SMALLPORTION, 

LARGEPORTION, and HEALTHCLAIM. The results are shown in appendix tables 1 and 2. When 

comparing two meals with an actual calorie difference of 200 or more, our representative consumer 

can still identify the higher calorie choice 84 percent of the time at fast-food restaurants and 73 

percent of the time at sit-down restaurants. These results are slightly less than estimates reported 

above for fast-food restaurants in table 3 (91 percent of the same pairwise comparisons) and sit-down 

restaurants in table 4 (80 percent of the same pairwise comparisons). 

In a second alternative specification of the model, our representative consumer is also unaware of 

two of the five rules. He or she does not know the caloric implications of choosing a fatty meat, 

nor does our consumer know to seek out meals with fruits and vegetables. To estimate a model 
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embodying these assumptions, we dropped FATTYMEAT and NOVEGGIES from the analysis. We 

then created the new variable, TOTALRULES2, which equals the sum of FRIES, HEAVYSAUCE, 

and DEEPFRIED. Finally, we replaced TOTALRULES with TOTALRULES2 in the above regression model. Estimation results are shown in appendix tables 3 and 4. We find that, when comparing 

two meals with an actual calorie difference of 200 calories or more, our representative consumer can 

identify the higher calorie choice 81 percent of the time at fast-food restaurants, but only 59 percent 

of the time at sit-down restaurants. 

Next, we considered two alternative specifications of the model in which our representative 

consumer has a more nuanced understanding of nutrition than captured by the explanatory variables 

in table 1. In the first of these two models, we added the variable BACON to our model for fast-food 

restaurants. This extra variable = 1 for burger and sandwich meals with added bacon, such as a 

bacon cheeseburger, and 0 for other meals. Results are shown in appendix table 5. Adding this variable to our model had little or no impact on our results.

Finally, in a specification of the model inspired by Chandon and Wansink’s (2007) “health halo” 

hypothesis, we accounted for the possibility that consumers may believe the meals offered by one 

restaurant to be generally healthier than the meals offered by the other restaurants. In our model 

for sit-down restaurants, we added binary indicator variables for five of the six chains under study. 

Results are shown in appendix table 6. Adding these variables to our model had little or no impact 

on our results.

Overall, having considered several alternative specifications of the model, it appears that consumers 

who understand the information outlined by the AHA and the NHLBI can discriminate fairly well 

between lower and higher choices. Nonetheless, rules of thumb are blunt tools, and menu labeling 

likewise imparts much additional information. Moreover, consumers who do not know the information outlined by the AHA and the NHLBI are likely to understand less about the calorie content of 

restaurant foods on their own, and they will gain relatively more new information when provided 

with explicit calorie information. 
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Conclusions

Menu-labeling laws require restaurant chains to post calorie information in a clear and conspicuous 

manner at the point of sale. This should enable consumers to better identify the meals that best 

satisfy their needs and wants. However, according to a growing body of research, many consumers 

do not adjust their food choices. Ellison et al. (2013) hypothesize that some of these consumers can 

already use their pre-existing understandings of nutrition to identify lower and higher calorie choices 

and, as such, gain little new information from menu labeling. Our findings extend this hypothesis. 

Behavioral economists and marketing scientists believe that consumers infer a product’s quality 

based on some observable characteristics. The AHA and the NHLBI further identify the observable 

characteristics of restaurant foods likely associated with calorie content. These associations may 

underlie rules of thumb that consumers can use for discriminating among restaurant foods. In this 

study, we test the efficacy of such simple heuristics. Results confirm that some consumers may be 

able to identify lower calorie from higher choices. For such consumers, some of the new information 

now being provided through menu-labeling laws is in fact “old” information. 

However, according to our study results, menu labeling is likely to improve all Americans’ abilities 

to weigh the taste, cost, and healthfulness of restaurant foods. It imparts additional information over 

general rules of thumb in at least a few ways. Heuristics are less effective for discriminating between 

foods that differ only modestly in calorie content. Moreover, when using rules of thumb, consumers 

may still underestimate the total calorie content of restaurant meals. Menu labeling will reveal the 

exact difference in calories between meals, even small differences, and include a statement about how 

those differences relate to an individual’s daily energy requirements. Overall, judging by these findings 

and the results of previous studies, many Americans may already be making crude choices between 

high- and low-calorie foods, based on their pre-existing understandings of nutrition. Menu labeling 

allows them to make finer adjustments in their food choices and behavior, if they choose to do so. 

That many consumers continue to order the same foods when exposed to calorie information does 

not imply that menu labeling fails to teach influential new information. A growing body of research 

investigates whether consumers order lower calorie foods at restaurants. Some do. Others appear 

unresponsive. One possible explanation is that, when more knowledgeable consumers compare two 

restaurant meals, they are only modestly surprised by the differences in calories. If so, according to 

Bordalo et al.’s (2012) theory of consumer choice, these discoveries may be insufficiently large to 

motivate a change in food choices. Of course, consumers could respond to menu labeling in other 

important ways. For example, understanding how the foods they purchase at restaurants fit within 

their daily caloric and other nutritional needs may motivate consumers to compensate by eating less 

at home or exercising more.12 Following an approach similar to Variyam and Cawley’s (2006) study 

of Americans’ body mass index before and after implementation of NLEA, future research could 

attempt to identify the effects of menu labeling on weight gain over time. 

12 There is some empirical support for this possibility. Roberto et al. (2010) find that placing a recommended daily 

caloric requirement label next to explicit calorie information did not impact calories consumed at a restaurant. However, 

it did encourage consumers to reduce their calorie intake over the course of the entire day, including foods consumed 

after leaving the restaurant.
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Appendix tables

Appendix table 1

Estimation results for calories in fast-food meals, first alternative model

OLS UQR

(10th centile)

UQR

(30th centile)

UQR

(60th centile)

UQR

(90th centile)

Constant 409.71*** 216.70*** 228.78*** 478.09*** 756.21***

(25.46) (48.62) (46.92) (42.45) (47.21)

TOTALRULES 145.46*** 104.21*** 173.86*** 152.77*** 174.79***

(10.19) (18.80) (15.90) (16.73) (41.32)

SMALLPORTION -205.17*** -209.34*** -259.18*** -276.92*** -133.04**

(19.84) (41.20) (34.20) (31.29) (37.17)

LARGEPORTION 465.11*** 11.51 96.83*** 375.78*** 1,390.31***

(30.30) (18.41) (27.07) (30.73) (174.48)

R2 0.66 0.20 0.36 0.39 0.42

Hit-and-miss analysis

 All combinations of 2 meals: 71.0 percent correctly predicted 

 Combinations of meals with 200(+) calorie difference: 83.9 percent correctly predicted 

OLS = Ordinary least squares regression

UQR = Unconditional quantile regression

R2 = Proportion of the variation in the dependent variable that is explained by the independent variables

Hit-and-miss analysis = A measure of the predictive accuracy of the model calculated using OLS results to estimate the 

number of calories in each meal. A selected meal’s predicted calorie content was then compared against that of the other 

meals. This process was repeated until all possible combinations of two different meals had been compared. Our model 

correctly predicted whether a selected meal was higher, lower, or equal in calories to another selected meal for 71.0 

percent of all pairwise comparisons. Moreover, if we restricted our hit-and-miss analysis to only combinations of 2 meals 

with an actual difference of 200 or more calories, then the percentage correctly predicted increased to 83.9 percent. 

Note: Standard errors are in parentheses. Statistically significant at the ***1-percent, **5-percent, and *10-percent levels.

Source: USDA, Economic Research Service calculations based on publicly available data.
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Appendix table 2

Estimation results for calories in sit-down restaurant meals, first alternative model

OLS UQR

(10th centile)

UQR

(30th centile)

UQR

(60th centile)

UQR

(90th centile)

Constant 710.51*** 367.78*** 421.91*** 722.81*** 1,235.07***

(7.39) (15.57) (11.89) (10.01) (12.03)

TOTALRULES 244.56*** 172.03*** 282.14*** 292.01*** 213.38***

(3.73) (6.70) (4.93) (5.06) (9.60)

HEALTHCLAIM -68.46*** -45.15*** -23.13 -99.76*** -74.17***

(9.46) (15.93) (15.23) (15.15) (15.24)

SMALLPORTION -295.53*** -138.67*** -301.67*** -402.04*** -276.15***

(12.96) (23.50) (22.13) (17.20) (12.92)

R2 0.46 0.16 0.33 0.31 0.12

Hit-and-miss analysis

 All combinations of 2 meals: 64.5 percent correctly predicted 

 Combinations of meals with 200(+) calorie difference: 73.4 percent correctly predicted 

OLS = Ordinary least squares regression

UQR = Unconditional quantile regression

R2 = Proportion of the variation in the dependent variable that is explained by the independent variables

Hit-and-miss analysis = A measure of the predictive accuracy of the model calculated using OLS results to estimate the 

number of calories in each meal. A selected meal’s predicted calorie content was then compared against that of the other 

meals. This process was repeated until all possible combinations of two different meals had been compared. Our model 

correctly predicted whether a selected meal was higher, lower, or equal in calories to another selected meal for 64.5 

percent of all pairwise comparisons. Moreover, if we restricted our hit-and-miss analysis to only combinations of 2 meals 

with an actual difference of 200 or more calories, then the percentage correctly predicted increased to 73.4 percent. 

Note: Standard errors are in parentheses. Statistically significant at the ***1-percent, **5-percent, and *10-percent levels.

Source: USDA, Economic Research Service calculations based on publicly available data.
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Appendix table 3

Estimation results for calories in fast-food meals, second alternative model

OLS UQR

(10th centile)

UQR

(30th centile)

UQR

(60th centile)

UQR

(90th centile)

Constant 466.87*** 263.43*** 320.97*** 552.05*** 1,378.85***

(23.45) (39.83) (43.12) (40.35) (10.78)

TOTALRULES2 166.45*** 115.67*** 184.14*** 166.18*** 206.19***

(12.62) (20.62) (18.36) (20.53) (12.09)

SMALLPORTION -201.31*** -206.61** -254.69 -272.93*** -54.14***

(20.39) (41.49) (36.68) (31.89) (15.28)

LARGEPORTION 521.34*** 51.50*** 162.83*** 434.11*** -201.15***

(31.01) (17.73) (24.98) (27.57) (10.46)

R2 0.64 0.18 0.31 0.37 0.42

Hit-and-miss analysis

 All combinations of 2 meals: 68.0 percent correctly predicted 

 Combinations of meals with 200(+) calorie difference: 80.6 percent correctly predicted 

OLS = Ordinary least squares regression

UQR = Unconditional quantile regression

R2 = Proportion of the variation in the dependent variable that is explained by the independent variables

Hit-and-miss analysis = A measure of the predictive accuracy of the model calculated using OLS results to estimate the 

number of calories in each meal. A selected meal’s predicted calorie content was then compared against that of the other 

meals. This process was repeated until all possible combinations of two different meals had been compared. Our model 

correctly predicted whether a selected meal was higher, lower, or equal in calories to another selected meal for 68.0 

percent of all pairwise comparisons. Moreover, if we restricted our hit-and-miss analysis to only combinations of 2 meals 

with an actual difference of 200 or more calories, then the percentage correctly predicted increased to 80.6 percent. 

Note: Standard errors are in parentheses. Statistically significant at the ***1-percent, **5-percent, and *10-percent levels.

Source: USDA, Economic Research Service calculations based on publicly available data.
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Appendix table 4

Estimation results for calories in sit-down restaurant meals, second alternative model

OLS UQR

(10th centile)

UQR

(30th centile)

UQR

(60th centile)

UQR

(90th centile)

Constant 896.27*** 502.31*** 626.95*** 955.09*** 1,378.85***

(7.68) (12.37) (11.76) (10.94) (10.78)

TOTALRULES2 216.61*** 148.75*** 258.61*** 248.00*** 206.19***

(5.85) (7.68) (7.43) (8.19) (12.09)

HEALTHCLAIM -54.22*** -36.73** -2.85 -87.11*** -54.14***

(11.44) (17.56) (16.74) (16.20) (15.28)

SMALLPORTION -210.19*** -78.76*** -202.95*** -300.47*** -201.15***

(15.34) (23.68) (22.75) (17.20) (10.46)

R2 0.24 0.08 0.17 0.16 0.07

Hit-and-miss analysis

All combinations of 2 meals: 53.2 percent correctly predicted 

Combinations of meals with 200(+) calorie difference: 58.7 percent correctly predicted 

OLS = Ordinary least squares regression

UQR = Unconditional quantile regression

R2 = Proportion of the variation in the dependent variable that is explained by the independent variables

Hit-and-miss analysis = A measure of the predictive accuracy of the model calculated using OLS results to estimate the 

number of calories in each meal. A selected meal’s predicted calorie content was then compared against that of the other 

meals. This process was repeated until all possible combinations of two different meals had been compared. Our model 

correctly predicted whether a selected meal was higher, lower, or equal in calories to another selected meal for 53.2 

percent of all pairwise comparisons. Moreover, if we restricted our hit-and-miss analysis to only combinations of 2 meals 

with an actual difference of 200 or more calories, then the percentage correctly predicted increased to 58.7 percent. 

Note: Standard errors are in parentheses. Statistically significant at the ***1-percent, **5-percent, and *10-percent levels.

Source: USDA, Economic Research Service calculations based on publicly available data.
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Appendix table 5

Estimation results for calories in fast-food meals, third alternative model

OLS UQR

(10th centile)

UQR

(30th centile)

UQR

(60th centile)

UQR

(90th centile)

Constant 498.3*** 254.86*** 316.33*** 617.04*** 857.14***

(26.1) (65.23) (57.48) (52.13) (66.14)

DEEPFRIED 87.4*** 68.99 147.87*** 56.8 77

(22.17) (59.47) (48.64) (48.53) (62.11)

HEAVYSAUCE 61.56*** 103.16** 62.63* 30.55 23.09

(19.82) (44.05) (36.85) (35.66) (61.11)

FRIES 325.86*** 154.55*** 310.62*** 343.03*** 430.02***

(17.12) (27.05) (26.98) (35.41) (83.4)

FATTYMEAT 71.33*** 32.28 129.14*** 42.61 65.02

(24.85) (69.72) (55.86) (54.72) (67.97)

BACON -16.55*** 5.02 -5.27 26.06 -81.88

(22.48) (40.47) (44.4) (49.64) (90.29)

NOVEGGIES -61.14** 67.2*** 44.24 -23.65 -284.41***

(27.27) (32.23) (38.17) (55.33) (99.05)

SMALLPORTION -221.17*** -211.51*** -283.09*** -297.52** -157.69***

(17.19) (40.82) (32.17) (31.8) (42.2)

LARGEPORTION 467.2*** 20.24*** 100.8*** 379.82** 1335.56***

(26.16) (23.99) (34.99) (38.48) (172.13)

R2 0.76 0.21 0.41 0.47 0.47

Hit-and-miss analysis

All combinations of 2 meals: 80.3 percent correctly predicted 

Combinations of meals with 200(+) calorie difference: 92.2 percent correctly predicted 

OLS = Ordinary least squares regression

UQR = Unconditional quantile regression

R2 = Proportion of the variation in the dependent variable that is explained by the independent variables

Hit-and-miss analysis = A measure of the predictive accuracy of the model calculated using OLS results to estimate the 

number of calories in each meal. A selected meal’s predicted calorie content was then compared against that of the other 

meals. This process was repeated until all possible combinations of two different meals had been compared. Our model 

correctly predicted whether a selected meal was higher, lower, or equal in calories to another selected meal for 80.3 

percent of all pairwise comparisons. Moreover, if we restricted our hit-and-miss analysis to only combinations of 2 meals 

with an actual difference of 200 or more calories, then the percentage correctly predicted increased to 92.2 percent. 

Note: Standard errors are in parentheses. Statistically significant at the ***1-percent, **5-percent, and *10-percent levels.

Source: USDA, Economic Research Service calculations based on publicly available data.
32

Menu Labeling Imparts New Information About the Calorie Content of Restaurant Foods, ERR-179

Economic Research Service/USDA

Appendix table 6

Estimation results for calories in sit-down restaurant meals, fourth alternative model

OLS UQR

(10th centile)

UQR

(30th centile)

UQR

(60th centile)

UQR

(90th centile)

Constant 792.84*** 288.62*** 461.65*** 905.08*** 1,334.01***

(29.55) (57.06) (46.61) (56.62) (70.56)

DEEPFRIED 131.95*** 188.02*** 305.97*** 129.66*** -68.51***

(11.72) (11.14) (16.89) (21.45) (20.52)

HEAVYSAUCE 155.44*** 92.87*** 174.29*** 198.03*** 154.02***

(6.79) (11.09) (10.78) (11.44) (13.65)

FRIES 268.21*** 175.31*** 305.39*** 280.73*** 306.31***

(7.44) (8.47) (10.23) (12.56) (19.95)

FATTYMEAT 316.56*** 233.15*** 356.49*** 427.88*** 199.75***

(7.93) (11.28) (12.19) (13.79) (18.22)

NOVEGGIES 186.49*** 138.25*** 173.63*** 198.25*** 200.69***

(13.84) (16.44) (19.55) (25.51) (40.74)

HEALTHCLAIM -144.23*** -88.88*** -119.02*** -176.20*** -129.73***

(9.82) (22.13) (23.02) (22.91) (21.54)

SMALLPORTION -342.77*** -183.79*** -364.34*** -461.52*** -294.46***

(23.24) (23.24) (22.90) (18.66) (16.19)

CHAIN2 4.25 43.52 -10.92 -74.54 22.48

(38.41) (70.86) (60.75) (73.76) (94.13)

CHAIN3 -34.43 127.14** 38.02 -178.30*** -25.72

(29.72) (56.75) (46.51) (56.86) (71.89)

CHAIN4 59.32* 162.81** 158.42*** -43.73 5.72

(33.19) (65.54) (54.21) (61.77) (74.13)

CHAIN5 -108.62*** 66.59 -77.03* -217.26*** -113.08

(29.31) (55.96) (45.88) (56.09) (71.23)

CHAIN6 32.47** 122.81** 56.60 -71.39 84.97

(30.58) (56.33) (47.96) (59.35) (77.24)

R2 0.52 0.18 0.37 0.35 0.15

Hit-and-miss analysis

 All combinations of 2 meals: 74.4 percent correctly predicted 

Combinations of meals with 200(+) calorie difference: 83.1 percent correctly predicted 

OLS = Ordinary least squares regression

UQR = Unconditional quantile regression

R2 = Proportion of the variation in the dependent variable that is explained by the independent variables

Hit-and-miss analysis = A measure of the predictive accuracy of the model calculated using OLS results to estimate the 

number of calories in each meal. A selected meal’s predicted calorie content was then compared against that of the other 

meals. This process was repeated until all possible combinations of two different meals had been compared. Our model 

correctly predicted whether a selected meal was higher, lower, or equal in calories to another selected meal for 74.4 

percent of all pairwise comparisons. Moreover, if we restricted our hit & miss analysis to only combinations of 2 meals with 

an actual difference of 200 or more calories, then the percentage correctly predicted increased to 83.1 percent. 

Note: Standard errors are in parentheses. Statistically significant at the ***1-percent, **5-percent, and *10-percent levels.

Source: USDA, Economic Research Service calculations based on publicly available data.